Finite element modeling with patient-specific geometry to assess clinical risks of percutaneous pulmonary valve implantation.

BACKGROUND: Percutaneous pulmonary valve implantation (PPVI) is a non-surgical treatment for right ventricular outflow tract (RVOT) dysfunction. During PPVI, a stented valve, delivered via catheter, replaces the dysfunctional pulmonary valve. Stent oversizing allows valve anchoring within the RVOT, but overexpansion can intrude on the surrounding structures. Potentially dangerous outcomes include aortic valve insufficiency (AVI) from aortic root (AR) distortion and myocardial ischemia from coronary artery (CA) compression. Currently, risks are evaluated via balloon angioplasty/sizing before stent deployment. Patient-specific finite element (FE) analysis frameworks can improve pre-procedural risk assessment, but current methods require hundreds of hours of high-performance computation. METHODS: We created a simplified method to simulate the procedure using patient-specific FE models for accurate, efficient pre-procedural PPVI (using balloon expandable valves) risk assessment. The methodology was tested by retrospectively evaluating the clinical outcome of 12 PPVI candidates. RESULTS: Of 12 patients (median age 14.5 years) with dysfunctional RVOT, 7 had native RVOT and 5 had RV-PA conduits. Seven patients had undergone successful RVOT stent/valve placement, three had significant AVI on balloon testing, one had left CA compression, and one had both AVI and left CA compression. A model-calculated change of more than 20% in lumen diameter of the AR or coronary arteries correctly predicted aortic valve sufficiency and/or CA compression in all the patients. CONCLUSION: Agreement between FE results and clinical outcomes is excellent. Additionally, these models run in 2-6 min on a desktop computer, demonstrating potential use of FE analysis for pre-procedural risk assessment of PPVI in a clinically relevant timeframe.

Validation of fluid-structure interaction simulations of the opening phase of phantom mitral heart valves under physiologically inspired conditions.

BACKGROUND AND OBJECTIVE: Atrioventricular valve disease is a common cause of heart failure, and successful surgical or interventional outcomes are crucial. Patient-specific fluid-structure interaction (FSI) modeling may provide valuable insights into valve dynamics and guidance of valve repair strategies. However, lack of validation has kept FSI modeling from clinical implementation. Therefore, this study aims to validate FSI simulations against in vitro benchmarking data, based on clinically relevant parameters for evaluating heart valve disease. METHODS: An FSI model that mimics the left heart was developed. The domain included a deformable mitral valve of different stiffnesses run with different inlet velocities. Five different cases were simulated and compared to in vitro data based on the pressure difference across the valve, the valve opening, and the velocity in the flow domain. RESULTS: The simulations underestimate the pressure difference across the valve by 6.8-14 % compared to catheter measurements. Evaluation of the valve opening showed an underprediction of 5.4-7.3 % when compared to cine MRI, 2D Echo, and 3D Echo data. Additionally, the simulated velocity through the valve showed a 7.9-8.4 % underprediction in relation to Doppler Echo measurements. Qualitative assessment of the velocity profile in the ventricle and the streamlines of the flow in the domain showed good agreement of the flow behavior. CONCLUSIONS: Parameters relevant to the diagnosis of heart valve disease estimated by FSI simulations showed good agreement when compared to in vitro benchmarking data, with differences small enough not to affect the grading of heart valve disease. The FSI model is thus deemed good enough for further development toward patient-specific cases.

Prediction of jaw opening function after mandibular reconstruction using subject-specific musculoskeletal modelling.

BACKGROUND: Mandibular reconstruction patients often suffer abnormalities in the mandibular kinematics. In silico simulations, such as musculoskeletal modelling, can be used to predict post-operative mandibular kinematics. It is important to validate the mandibular musculoskeletal model and analyse the factors influencing its accuracy. OBJECTIVES: To investigate the jaw opening-closing movements after mandibular reconstruction, as predicted by the subject-specific musculoskeletal model, and the factors influencing its accuracy. METHODS: Ten mandibular reconstruction patients were enrolled in this study. Cone-beam computed tomography images, mandibular movements, and surface electromyogram signals were recorded preoperatively. A subject-specific mandibular musculoskeletal model was established to predict surgical outcomes using patient-averaged muscle parameter changes as model inputs. Jaw bone geometry was replaced by surgical planning results, and the muscle insertion sites were registered based on the non-rigid iterative closest point method. The predicted jaw kinematic data were validated based on 6-month post-operative measurements. Correlations between the prediction accuracy and patient characteristics (age, pathology and surgical scope) were further analysed. RESULTS: The root mean square error (RMSE) for lower incisor displacement was 31.4%, and the error for peak magnitude of jaw opening was 4.9 mm. Age, post-operative infection and radiotherapy influenced the prediction accuracy. The amount of masseter detachment showed little correlation with jaw opening. CONCLUSION: The mandibular musculoskeletal model successfully predicted short-range jaw opening functions after mandibular reconstruction. It provides a novel surgical planning method to predict the risk of developing trismus.

Virtual Shape-Editing of Patient-Specific Vascular Models Using Regularized Kelvinlets.

OBJECTIVE: Cardiovascular diseases, and the interventions performed to treat them, can lead to changes in the shape of patient vasculatures and their hemodynamics. Computational modeling and simulations of patient-specific vascular networks are increasingly used to quantify these hemodynamic changes, but they require modifying the shapes of the models. Existing methods to modify these shapes include editing 2D lumen contours prescribed along vessel centerlines and deforming meshes with geometry-based approaches. However, these methods can require extensive by-hand prescription of the desired shapes and often do not work robustly across a range of vascular anatomies. To overcome these limitations, we develop techniques to modify vascular models using physics-based principles that can automatically generate smooth deformations and readily apply them across different vascular anatomies. METHODS: We adapt Regularized Kelvinlets, analytical solutions to linear elastostatics, to perform elastic shape-editing of vascular models. The Kelvinlets are packaged into three methods that allow us to artificially create aneurysms, stenoses, and tortuosity. RESULTS: Our methods are able to generate such geometric changes across a wide range of vascular anatomies. We demonstrate their capabilities by creating sets of aneurysms, stenoses, and tortuosities with varying shapes and sizes on multiple patient-specific models. CONCLUSION: Our Kelvinlet-based deformers allow us to edit the shape of vascular models, regardless of their anatomical locations, and parametrically vary the size of the geometric changes. SIGNIFICANCE: These methods will enable researchers to more easily perform virtual-surgery-like deformations, computationally explore the impact of vascular shape on patient hemodynamics, and generate synthetic geometries for data-driven research.

Modeling and evaluation of biomechanics and hemodynamic based on patient-specific small intracranial aneurysm using fluid-structure interaction.

BACKGROUND AND OBJECTIVE: Rupture of small intracranial aneurysm (IA) often leads to the development of highly fatal clinical syndromes such as subarachnoid hemorrhage. Due to the patient specificity of small IA, there are many difficulties in evaluating the rupture risk of small IA such as multiple influencing factors, high clinical experience requirements and poor reusability. METHODS: In this study, clinical methods such as transcranial doppler (TCD) and magnetic resonance imaging (MRI) are used to obtain patient-specific parameters, and the fluid-structure interaction method (FSI) is used to model and evaluate the biomechanics and hemodynamics of patient-specific small IA. RESULTS: The results show that a spiral vortex stably exists in the patient-specific small IA. Due to the small size of the patient-specific small IA, the blood flow velocity still maintains a high value with maximum reaching 3 m/s. The inertial impact of blood flow and vortex convection have certain influence on hemodynamic and biomechanics parameters. They cause three high value areas of WSSM on the patient-specific small IA with maximum of 180 Pa, 130 Pa and 110 Pa, respectively. They also cause two types of WSS concentration points, positive normal stress peak value areas and negative normal stress peak value areas to appear. CONCLUSION: This paper found that the factors affecting hemodynamic parameters and biomechanical parameters are different. Unlike hemodynamic parameters, biomechanical parameters are also affected by blood pressure in addition to blood flow velocity. This study reveals the relationship between the flow field distribution and changes of patient-specific small IA, biomechanics and hemodynamics.

Patient-Specific Heart Geometry Modeling for Solid Biomechanics Using Deep Learning.

Automated volumetric meshing of patient-specific heart geometry can help expedite various biomechanics studies, such as post-intervention stress estimation. Prior meshing techniques often neglect important modeling characteristics for successful downstream analyses, especially for thin structures like the valve leaflets. In this work, we present DeepCarve (Deep Cardiac Volumetric Mesh): a novel deformation-based deep learning method that automatically generates patient-specific volumetric meshes with high spatial accuracy and element quality. The main novelty in our method is the use of minimally sufficient surface mesh labels for precise spatial accuracy and the simultaneous optimization of isotropic and anisotropic deformation energies for volumetric mesh quality. Mesh generation takes only 0.13 seconds/scan during inference, and each mesh can be directly used for finite element analyses without any manual post-processing. Calcification meshes can also be subsequently incorporated for increased simulation accuracy. Numerous stent deployment simulations validate the viability of our approach for large-batch analyses. Our code is available at https://github.com/danpak94/Deep-Cardiac-Volumetric-Mesh.

Patient-Specific Modeling and Model Predictive Control Approach to Personalized Optimal Anemia Management.

In the condition of anemia, kidneys produce less erythropoietin hormone to stimulate the bone marrow to make red blood cells (RBC) leading to a reduced hemoglobin (Hgb) level, also known as chronic kidney disease (CKD). External recombinant human erythropoietin (EPO) is administrated to maintain a healthy level of Hgb, i.e., 10 - 12 g/dl. The semi-blind robust model identification method is used to obtain a personalized patient model using minimum dose-response data points. The identified patient models are used as predictive models in the model predictive control (MPC) framework. The simulation results of MPC for different CKD patients are compared with those obtained from the existing clinical method, known as anemia management protocol (AMP), used in hospitals. The in-silico results show that MPC outperforms AMP to maintain healthy levels of Hgb without over-or-under- shoots. This offers a considerable performance improvement compared to AMP which is unable to stabilize EPO dosage and shows oscillations in Hgb levels throughout the treatment.Clinical Relevance-This research work provides a framework to help clinicians in decision-making for personalized EPO dose guidance using MPC with semi-blind robust model identification using minimum clinical patient dose-response data.

3D patient-specific modeling and structural finite element analysis of atherosclerotic carotid artery based on computed tomography angiography.

The assessment of carotid plaque vulnerability is a relevant clinical information that can help prevent adverse cerebrovascular events. To this aim, in this study, we propose a patient-specific computational workflow to quantify the stress distribution in an atherosclerotic carotid artery, by means of geometric modeling and structural simulation of the plaque and vessel wall. Ten patients were involved in our study. Starting with segmentation of the lumen, calcific and lipid plaque components from computed tomography angiography images, the fibrous component and the vessel wall were semi-automatically reconstructed with an ad-hoc procedure. Finite element analyses were performed using local pressure values derived from ultrasound imaging. Simulation outputs were analyzed to assess how mechanical factors influence the stresses within the atherosclerotic wall. The developed reconstruction method was first evaluated by comparing the results obtained using the automatically generated fibrous component model and the one derived from image segmentation. The high-stress regions in the carotid artery wall around plaques suggest areas of possible rupture. In mostly lipidic and heterogeneous plaques, the highest stresses are localized at the interface between the lipidic components and the lumen, in the fibrous cap.

A Narrative Review of Personalized Musculoskeletal Modeling Using the Physiome and Musculoskeletal Atlas Projects.

In this narrative review, we explore developments in the field of computational musculoskeletal model personalization using the Physiome and Musculoskeletal Atlas Projects. Model geometry personalization; statistical shape modeling; and its impact on segmentation, classification, and model creation are explored. Examples include the trapeziometacarpal and tibiofemoral joints, Achilles tendon, gastrocnemius muscle, and pediatric lower limb bones. Finally, a more general approach to model personalization is discussed based on the idea of multiscale personalization called scaffolds.

Bio-priming with a Novel Plant Growth-Promoting Acinetobacter indicus Strain Alleviates Arsenic-Fluoride Co-toxicity in Rice by Modulating the Physiome and Micronutrient Homeostasis.

Sustainable remediation of arsenic-fluoride from rice fields through efficient bio-extraction is the need of the hour, since these toxicants severely challenge safe cultivation of rice and food biosafety. In the present study, we screened an arsenic-fluoride tolerant strain AB-ARC of Acinetobacter indicus from the soil of a severely polluted region of West Bengal, India, which was capable of efficiently removing extremely high doses of arsenate and fluoride from the media. The strain also behaved as a plant growth-promoting rhizobacterium, since it could produce indole-3-acetic acid and solubilize phosphate, zinc, and starch. Due to these properties of the identified strain, it was used for bio-priming the seeds of the arsenic-fluoride susceptible rice cultivar, Khitish for testing the efficacy of the AB-ARC strain to promote combined arsenic-fluoride tolerance in the rice genotype. Bio-priming with AB-ARC led to accelerated uptake of crucial elements like iron, copper, and nickel which behave as co-factors of physiological and antioxidative enzymes. Thus, the activation of superoxide dismutase, catalase, guaiacol peroxidase, glutathione peroxidase, and glutathione-S-transferase enabled detoxification of reactive oxygen species (ROS) and reduction of the oxidative injuries like malondialdehyde and methylglyoxal generation. Overall, due to ameliorated molecular damages and low uptake of the toxic xenobiotics, the plants were able to maintain improved growth vigor and photosynthesis, as evident from the elevated levels of Hill activity and chlorophyll content. Hence, bio-priming with the A. indicus AB-ARC strain may be advocated for sustainable rice cultivation in arsenic-fluoride co-polluted fields.

A systematic review of follow-up results of additively manufactured customized implants for the pelvic area.

INTRODUCTION: While 3D printing of bone models for preoperative planning or customized surgical templating has been successfully implemented, the use of patient-specific additively manufactured (AM) implants is a newer application not yet well established. To fully evaluate the advantages and shortcomings of such implants, their follow-up results need to be evaluated. AREA COVERED: This systematic review provides a survey of the reported follow-ups on AM implants used for oncologic reconstruction, total hip arthroplasty both primary and revision, acetabular fracture, and sacrum defects. EXPERT OPINION: The review shows that Titanium alloy (Ti4AL6V) is the most common type of material system used due to its excellent biomechanical properties. Electron beam melting (EBM) is the predominant AM process for manufacturing implants. In almost all cases, porosity at the contact surface is implemented through the design of lattice or porous structures to enhance osseointegration. The follow-up evaluations show promising results, with only a small number of patients suffering from aseptic loosening, wear, or malalignment. The longest reported follow-up length was 120 months for acetabular cages and 96 months for acetabular cups. The AM implants have proven to serve as an excellent option to restore premorbid skeletal anatomy of the pelvis.

Can the response to dialysis treatment be predicted by using patient-specific modeling of fluid and solute exchanges? A multicentric evaluation.

BACKGROUND: Parametric multipool kinetic models were used to describe the intradialytic trends of electrolytes, breakdown products, and body fluids volumes during hemodialysis. Therapy customization can be achieved by the identification of parameters, allowing patient-specific modulation of mass and fluid balance across dialyzer, capillary, and cell membranes. This study wants to evaluate the possibility to use this approach to predict the patient's intradialytic response. METHODS: 6 sessions of 68 patients (DialysIS© project) were considered. Data from the first three sessions were used to train the model, identifying the patient-specific parameters, that, together with the treatment settings and the patient's data at the session start, could be used for predicting the patient's specific time course of solutes and fluids along the sessions. Na+ , K+ , Cl- , Ca2+ , HCO3 - , and urea plasmatic concentrations and hematic volume deviations from clinical data were evaluated. RESULTS: nRMSE predictive error is on average equal to 4.76% when describing the training sessions, and only increases by 0.97 percentage points on average in independent sessions of the same patient. CONCLUSIONS: The proposed predictive approach represents a first step in the development of tools to support the clinician in tailoring the patient's prescription.

Biomechanical consequences of the intervertebral disc centre of rotation kinematics during lateral bending and axial rotation.

The location of the instantaneous centre of rotation (ICR) of a lumbar unit has a considerable clinical importance as a spinal health estimator. Consequently, many studies have been conducted to measure or estimate the ICR during rotations in the three anatomical planes; however the results reported are widely scattered. Even if some inter-subjects variability is to be expected, such inconsistencies are likely explained by the differences in methods and experiments. Therefore, in this paper we seek to model three behaviours of the ICR during lateral bending and axial rotation based on results published in the literature. In order to assess the metabolic and mechanical sensibility to the assumption made on the ICR kinematics, we used a previously validated three dimensional non-linear poroelastic model of a porcine intervertebral disc to simulate physiological lateral and axial rotations. The impact of the geometry was also briefly investigated by considering a 11[Formula: see text] wedge angle. From our simulations, it appears that the hypothesis made on the ICR location does not significantly affect the critical nutrients concentrations but gives disparate predictions of the intradiscal pressure at the centre of the disc (variation up to 0.7 MPa) and of the displacement fields (variation up to 0.4 mm). On the contrary, the wedge angle does not influence the estimated intradiscal pressure but leads to minimal oxygen concentration decreased up to 33% and increased maximal lactate concentration up to 13%. While we can not settle on which definition of the ICR is more accurate, this work suggests that patient-specific modeling of the ICR is required and brings new insights that can be useful for the development of new tools or the design of surgical material such as total lumbar disc prostheses.

Optimization framework for patient-specific modeling under uncertainty.

Estimating a patient-specific computational model's parameters relies on data that is often unreliable and ill-suited for a deterministic approach. We develop an optimization-based uncertainty quantification framework for probabilistic model tuning that discovers model inputs distributions that generate target output distributions. Probabilistic sampling is performed using a surrogate model for computational efficiency, and a general distribution parameterization is used to describe each input. The approach is tested on seven patient-specific modeling examples using CircAdapt, a cardiovascular circulatory model. Six examples are synthetic, aiming to match the output distributions generated using known reference input data distributions, while the seventh example uses real-world patient data for the output distributions. Our results demonstrate the accurate reproduction of the target output distributions, with a correct recreation of the reference inputs for the six synthetic examples. Our proposed approach is suitable for determining the parameter distributions of patient-specific models with uncertain data and can be used to gain insights into the sensitivity of the model parameters to the measured data.

Designing a Novel Asymmetric Transcatheter Aortic Valve for Stenotic Bicuspid Aortic Valves Using Patient-Specific Computational Modeling.

Bicuspid aortic valve (BAV), the most common congenital heart malformation, is characterized by the presence of only two valve leaflets with asymmetrical geometry, resulting in elliptical systolic opening. BAV often leads to early onset of calcific aortic stenosis (AS). Following the rapid expansion of transcatheter aortic valve replacement (TAVR), designed specifically for treating conventional tricuspid AS, BAV patients with AS were initially treated "off-label" with TAVR, which recently gained FDA and CE regulatory approval. Despite its increasing use in BAV, pathological BAV anatomy often leads to complications stemming from mismatched anatomical features. To mitigate these complications, a novel eccentric polymeric TAVR valve incorporating asymmetrical leaflets was designed specifically for BAV anatomies. Computational modeling was used to optimize its asymmetric leaflets for lower functional stresses and improved hemodynamic performance. Deployment and flow were simulated in patient-specific BAV models (n = 6) and compared to a current commercial TAVR valve (Evolut R 29 mm), to assess deployment and flow parameters. The novel eccentric BAV-dedicated valve demonstrated significant improvements in peak systolic orifice area, along with lower jet velocity and wall shear stress (WSS). This feasibility study demonstrates the clinical potential of the first known BAV-dedicated TAVR design, which will foster advancement of patient-dedicated valvular devices.

Patient-Specific Computational Modeling of Different Cannulation Strategies for Extracorporeal Membrane Oxygenation.

Institution of extracorporeal membrane oxygenation (ECMO) results in unique blood flow characteristics to the end-organ vascular beds. We studied the interplay between cardiac-driven and extracorporeal membrane oxygenation (ECMO)-driven flow to vascular beds in different ECMO configurations using a patient-specific computational fluid dynamics (CFD) analysis. A computational ECMO model (femoral artery cannulation [FAC]) was constructed using patient-specific imaging and hemodynamic data. Following model calibration, we augmented the 3D geometrical model to represent alternative ECMO configurations (ascending aorta cannulation [AAC] and subclavian artery cannulation [SAC]). We performed CFD analyses, including a novel virtual color-dye analysis to compare global and regional blood flow and pressure characteristics as well as contributions of cardiac and ECMO-derived flow to the various vascular beds. Flow waveforms at all the aortic branch vessels were pulsatile, despite low cardiac output and predominant nonpulsatile ECMO-driven hemodynamics. Virtual color-dye analysis revealed differential contribution of cardiac and ECMO-derived flow to the end-organ vascular beds in the FAC model, while this was more evenly distributed in the AAC and SAC models. While global hemodynamics were relatively similar between various ECMO configurations, several distinct hemodynamic indices, in particular wall shear stress and oscillatory shear patterns, as well as differential contribution of ECMO-derived flow to various vascular beds, showed remarkable differences. The clinical impact of this study highlighting the relevance of CFD modeling in assessment of complex hemodynamics in ECMO warrants further evaluation.

Credibility assessment of patient-specific computational modeling using patient-specific cardiac modeling as an exemplar.

Reliable and robust simulation of individual patients using patient-specific models (PSMs) is one of the next frontiers for modeling and simulation (M&S) in healthcare. PSMs, which form the basis of digital twins, can be employed as clinical tools to, for example, assess disease state, predict response to therapy, or optimize therapy. They may also be used to construct virtual cohorts of patients, for in silico evaluation of medical product safety and/or performance. Methods and frameworks have recently been proposed for evaluating the credibility of M&S in healthcare applications. However, such efforts have generally been motivated by models of medical devices or generic patient models; how best to evaluate the credibility of PSMs has largely been unexplored. The aim of this paper is to understand and demonstrate the credibility assessment process for PSMs using patient-specific cardiac electrophysiological (EP) modeling as an exemplar. We first review approaches used to generate cardiac PSMs and consider how verification, validation, and uncertainty quantification (VVUQ) apply to cardiac PSMs. Next, we execute two simulation studies using a publicly available virtual cohort of 24 patient-specific ventricular models, the first a multi-patient verification study, the second investigating the impact of uncertainty in personalized and non-personalized inputs in a virtual cohort. We then use the findings from our analyses to identify how important characteristics of PSMs can be considered when assessing credibility with the approach of the ASME V&V40 Standard, accounting for PSM concepts such as inter- and intra-user variability, multi-patient and "every-patient" error estimation, uncertainty quantification in personalized vs non-personalized inputs, clinical validation, and others. The results of this paper will be useful to developers of cardiac and other medical image based PSMs, when assessing PSM credibility.

Patient-specific modeling of stroma-mediated chemoresistance of pancreatic cancer using a three-dimensional organoid-fibroblast co-culture system.

BACKGROUND: Cancer-associated fibroblasts (CAFs) are considered to play a fundamental role in pancreatic ductal adenocarcinoma (PDAC) progression and chemoresistance. Patient-derived organoids have demonstrated great potential as tumor avatars for drug response prediction in PDAC, yet they disregard the influence of stromal components on chemosensitivity. METHODS: We established direct three-dimensional (3D) co-cultures of primary PDAC organoids and patient-matched CAFs to investigate the effect of the fibroblastic compartment on sensitivity to gemcitabine, 5-fluorouracil and paclitaxel treatments using an image-based drug assay. Single-cell RNA sequencing was performed for three organoid/CAF pairs in mono- and co-culture to uncover transcriptional changes induced by tumor-stroma interaction. RESULTS: Upon co-culture with CAFs, we observed increased proliferation and reduced chemotherapy-induced cell death of PDAC organoids. Single-cell RNA sequencing data evidenced induction of a pro-inflammatory phenotype in CAFs in co-cultures. Organoids showed increased expression of genes associated with epithelial-to-mesenchymal transition (EMT) in co-cultures and several potential receptor-ligand interactions related to EMT were identified, supporting a key role of CAF-driven induction of EMT in PDAC chemoresistance. CONCLUSIONS: Our results demonstrate the potential of personalized PDAC co-cultures models not only for drug response profiling but also for unraveling the molecular mechanisms involved in the chemoresistance-supporting role of the tumor stroma.

The pathophysiology of major depressive disorder through the lens of systems biology: Network analysis of the psycho-immune-neuroendocrine physiome.

BACKGROUND/AIMS: The psycho-immune-neuroendocrine (PINE) network is a predominantly physiological (metabolomic) model constructed from the literature, inter-linking multiple biological processes associated with major depressive disorder (MDD), thereby integrating putative mechanistic pathways for MDD into a single network. MATERIAL AND METHODS: Previously published metabolomic pathways for the PINE network based on literature searches conducted in 1991-2021 were used to construct an edge table summarizing all physiological pathways in pairs of origin nodes and target nodes. The Gephi software program was used to calculate network metrics from the edge table, including total degree and centrality measures, to ascertain key network nodes and construct a directed network graph. RESULTS: An edge table and directional network graph of physiological relationships in the PINE network is presented. The network has properties consistent with complex biological systems, with analysis yielding key network nodes comprising pro-inflammatory cytokines (TNF- α, IL6 and IL1), glucocorticoids and corticotropin releasing hormone (CRH). These may represent central structural and regulatory elements in the context of MDD. CONCLUSION: The identified hubs have a high degree of connection and are known to play roles in the progression from health to MDD. These nodes represent strategic targets for therapeutic intervention or prevention. Future work is required to build a weighted and dynamic simulation of the network PINE.